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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15262))

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Abstract

Model-Based Testing (mbt) has grown from testing infamous coffee machines to complex industrial size systems with many interfaces and parallel message exchanges.

Over the last decade Axini has developed a state-of-the-art platform, which can be used to model, analyze and test (very) large systems. We have successfully applied our platform to several medium to large applications in the finance and high-tech industry.

While modeling and testing such systems, we stumbled upon quality, management and engineering challenges to cope with the complexity of the whole mbt trajectory. We have developed several engineering guidelines to control both our modeling and testing trajectories.

Moreover, over the last few years, we have witnessed that model based working has gotten more acceptance within the industry. For mbt we see a shift of focus from testing to modeling. Originally, mbt would be applied when the development of the System Under Test was completely finished. Currently, we start the modeling during the analysis and design phase of the system. We have adapted our engineering methodology and guidelines accordingly.

We share our lessons learned and provide valuable insights for modeling and test engineers involved in model based testing and/or development.

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Notes

  1. 1.

    The term ‘tracer bullet’ is a familiar term in software engineering [18] and is borrowed from flammable ammunition that gunners use to plot the trajectory of their shots.

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Acknowledgments

amp is already in development for more than a decade. Many current and former employees of Axini have worked on our toolset, and made it possible to apply mbt to industrial size systems. The internal discussions on structuring and standardizing the mbt trajectory were the basis for this paper.

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Correspondence to Theo C. Ruys .

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Ruys, T.C., van der Bijl, M. (2025). Effective Model-Based Testing. In: Jansen, N., et al. Principles of Verification: Cycling the Probabilistic Landscape . Lecture Notes in Computer Science, vol 15262. Springer, Cham. https://doi.org/10.1007/978-3-031-75778-5_11

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  • DOI: https://doi.org/10.1007/978-3-031-75778-5_11

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